Data Analyst

Role overview

This position is for a Machine Learning Engineer within the Data Analyst title. The role focuses on taking machine learning solutions from experimentation to production by combining data engineering, software engineering, cloud, and DevOps practices. The aim is to improve model quality, enable scalable deployment, and support business use cases through reliable data and machine learning platforms.

Key responsibilities

The role covers engineering support across the machine learning lifecycle, analytics, data management, programming, visualization, and testing.

• Contribute to data platform components that handle distributed data processing, scalable feature stores, and monitoring for data health and alerts.
• Support the machine learning platform with distributed model training, evaluation, observability, and performance monitoring.
• Help build and maintain end-to-end machine learning workflows, including MLOps processes.
• Work alongside data scientists and data engineers to operationalize data pipelines and machine learning models for business use.
• Assist in preparing final-stage data readiness, such as embeddings and curated features, so data scientists can move faster toward model value creation.
• Support the use of machine learning methods with data scientists and domain experts to improve model performance while respecting responsible AI requirements.
• Help select, gather, and integrate data features needed for analysis.
• Support unsupervised learning methods, such as clustering, to discover patterns and prepare for supervised learning tasks.
• Assist with exploratory data analysis, data design, data modeling, and quality checks for curated AI features.
• Support data engineering work needed to deliver projects successfully.
• Help implement physical database and data warehouse designs that support feature availability for the model lifecycle.
• Assist in ensuring data remains accessible, retrievable, secure, and ethically protected.
• Design, code, test, document, modify, and refactor moderately complex scripts and programs.
• Support development and deployment of feature engineering and model training/inference code using CI/CD practices.
• Help build cloud-native and on-premise MDLC templates that coordinate development work across engineering teams.
• Support model development and deployment in distributed computing and big data environments to handle volume, velocity, and variety challenges.
• Use different visualization methods to present data clearly for storytelling and exploratory analysis.
• Assist in automating dashboards and reports for timely and efficient visualization delivery.
• Help communicate the results of unsupervised learning outputs, such as clustering, to explain hidden patterns in data.
• Review requirements and specifications and define suitable test conditions.
• Support test case and test script design, execute checks against defined criteria, and record outcomes.
• Analyze and report testing activities and results.
• Identify and report issues and risks related to assigned work.
• Support the creation of unit, integration, and regression tests within CI/CD workflows for MDLC.

Education and experience

The employer is looking for a candidate with a bachelor’s degree in Computer Science, Mathematics, or a similar discipline. A master’s degree is considered an advantage. The role calls for at least 4 years of practical experience in machine learning engineering or a closely related field. Experience with financial or demand planning data is beneficial. Prior internship exposure during college is preferred, though it is not required.

Technical qualifications

Strong knowledge of data structures, data modeling, and software architecture is expected. The role also requires hands-on exposure to production-grade MLOps, including feature engineering, distributed training, serving, and inference. Experience with big data tools and platforms such as Apache Kafka, Apache Spark, AWS EMR, Databricks, and related MLOps tooling is valuable, along with some exposure to GenAI. Strong Python coding ability is important, as well as familiarity with machine learning frameworks such as Keras or PyTorch, libraries like scikit-learn, and tools such as AWS SageMaker, TensorFlow, and NLP methods.

Soft skills

The position calls for clear, concise written and verbal communication in English, strong resilience, a solution-focused mindset, and the ability to adapt academic ideas or research findings into practical data science solutions.

Workplace values

The organization states that it values diversity, inclusion, and individual expression. Harassment and discrimination are not tolerated, and the employer identifies as an equal opportunity employer.